METHOD FOR IDENTIFYING AND CHARACTERIZING, BY USING ARTIFICIAL INTELLIGENCE, NOISES GENERATED BY A VEHICLE BRAKING SYSTEM

A method for identifying and characterizing noises generated by a vehicle braking system is described. The method first comprises the steps of detecting noises generated by a vehicle braking system under dynamic operating conditions and generating digital audio data representative of the detected noise. The method then provides analyzing the aforesaid digital audio data by a noise analyzer, to identify potential squeal events and respective likely squeal frequencies, and generating squeal frequency information indicative of the squeal frequencies of the identified potential squeal events. The method then comprises the steps of filtering the aforesaid digital audio data by means of high-pass filtering to eliminate spectral components at frequencies lower than a filtering frequency, to generate filtered digital audio data; and generating, based on the filtered digital audio data, a respective spectrogram, which represents, in graphical form, information present in the filtered digital audio data, comprising the sound signal intensity, as a function of time and frequency. The method then involves providing the aforesaid spectrogram and the aforesaid squeal frequency information to a trained algorithm, wherein the algorithm was trained using artificial intelligence and/or machine learning techniques. The method also provides identifying noise events, by the trained algorithm, based on the above spectrogram and squeal frequency information, classifying the identified noise events and finally providing information about the identified noise events, each characterized by the respective category. The aforesaid classification step involves a classification according to at least the following categories: a first category comprising noises to be detected generated by the characteristic dynamic operation of the braking system; and a second category comprising abnormal noises, generated by operational or test anomalies.